The present invention relates to wireline operations, and more particularly, to a system and method for a cloud logging system for wireline logging data acquisition and tool processing.
Hydrocarbons, such as oil and gas, are commonly obtained from subterranean formations. Although systems for wireline logging data acquisition and tool processing are known, these systems may not be able to handle more complex tool processing. Generally, systems for wireline logging data acquisition and tool processing include a single computing device at a well-site. These systems depend on the installed computer hardware available at the well-site to perform the logging data and tool processing.
Typically, subterranean operations involve a number of different steps such as, for example, drilling the wellbore at a desired well site, treating the wellbore to optimize production of hydrocarbons, and performing the necessary steps to produce and process the hydrocarbons from the subterranean formation.
Each of these different steps involve a plurality of tool processing and logging data acquisition provided by one or more information provider units, which provide measurements and data relating to the operation of the well-site. These measurements may include surface measurements and data may further comprise downhole telemetry data. The information provider units may include units such as the wireline drum, the managed pressure drilling unit (MPD), underbalanced pressure drilling unit, fluid skid, measurement while drilling (MWD) toolbox, and other such systems. Generally, for operation of a wellsite, it is required that parameters be measured from each of the information provider units at a wellsite.
Traditionally, logging data acquisition and tool processing are being performed by the computing device installed at the well-site. The range of processing computer power required to perform the various tool processing can range from the very simple to more complex and computing intensive processing tasks. However, because the tool processing is performed by the computing device available at the well-site, each well-site must have the capability to perform the most complex tool processing requiring the most sophisticated hardware available for the computing device. Additionally, as tool processing generates more complex and time consuming results, the hardware at these well-sites must be updated in each logging unit that performs logging data acquisition and tool processing. Such upgrades can be very costly to the management of a well-site.
Alternatively, another solution that has been previously proposed to address the problem of more complex tool processing is to perform simple functions at the well-site and reprocess the data later in a remote computer center. Such a system also has drawbacks in that the system only provides for minimal processing at the well-site location. This may slow down efforts at the well-site resulting in significant expense by delay of operation at the well-site. This solution also fails to offer a real-time solution to the problem of complex tool processing emerging in the field.
Another alternative would be to employ multiple computers, one to acquire and gather tool processing data, and another to reprocess data as it arrives. However, the limitation with this solution is that it will raise the expense of having multiple devices at each well-site dedicated to this task. With space being at a premium at a well-site, this solution also has limitations.
These processes of collecting the data from the various information provider units and tool processing can be time-consuming, cumbersome, and inefficient. With the increasing demand for hydrocarbons and the desire to minimize the costs associated with performing rig operations, there exists a need for a cloud logging system to handle simple to complex logging data acquisition and tool processing. The cloud logging system also eliminates the need for the end user to have knowledge of the physical location and configuration of the computing devices and the increases needed in computing capacity.